Can we automate spine fusion surgical site infection data capture?

Abstract

Spine fusion surgical site infection (SSI) rate is reported to national quality databases and used as a benchmark for orthopedic departments and hospital systems. However, accurate data require resource-heavy administrative review and even this has shown to vary. We aimed to create a passive electronic medical record (EMR) algorithm to automatically capture spine fusion SSI and compared its accuracy against the administrative chart review and self-reported morbidity and mortality (M&M) rates. We retrospectively reviewed a single institution's spine fusion records for 7years for all 90-day post-operative SSIs. We used Centers for Disease Control and Prevention (CDC) SSI definition coupled with intention to treat as an infection by orthopedics/infectious disease service as the gold standard. We compared our gold standard to administrative hand-checked SSI data, anonymously reported departmental M&M, and a passive EMR algorithm (ICD-9 or -10 post-operative SSI diagnosis code entered, or all four of: positive culture, antibiotic prescription between 3-90days post-op, re-operation/re-admission, and a qualifying diagnosis). Nine hundred and fourteen spine fusions were included, with a 2.8% SSI rate (0.9% superficial and 2.0% deep). Passive EMR algorithm was the most sensitive at 89% (vs 76% administrative review, 73% M&M); all were highly specific at 99-100%. M&M was 100% positively predictive, administrative review 95%, and EMR 79%. Our passive EMR algorithm was more sensitive to pediatric spine fusion 90-day SSI than self-reported M&M and hand-checked administrative chart review. Although EMR may over-report, it can be used by others to narrow the initial sample for review, reduce resource burden involved with administrative spine SSI review, and provide a quality check for M&M self-reporting. III.